Summary of Mod-cl: Multi-label Object Detection with Constrained Loss, by Sota Moriyama et al.
MOD-CL: Multi-label Object Detection with Constrained Loss
by Sota Moriyama, Koji Watanabe, Katsumi Inoue, Akihiro Takemura
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MOD-CL framework is a multi-label object detection model that leverages constrained loss during training to generate outputs that better meet specific requirements. Built upon YOLOv8, the framework incorporates two new models, Corrector and Blender, which refine object detection outputs. The paper also introduces constrained losses into the architecture using Product T-Norm for Task 2, yielding improved scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a more accurate object detection system that can produce multiple labels per object. By introducing constraints during training, the MOD-CL framework can better satisfy given requirements, leading to better object detection outcomes. |
Keywords
» Artificial intelligence » Object detection
